code: def test_switch_training(
      max_steps: int = 10, threshold: float = 0.9, num_experts=5
  ):
      dataset = load_digits(n_class=2)
      X_train, y_train = torch.tensor(
          dataset["data"], dtype=torch.float
      ), torch.tensor(dataset["target"])
      subsample_ix = torch.randint(0, len(X_train), (32,))
      X_train, y_train = X_train[subsample_ix], y_train[subsample_ix]
  
      SGD = partial(torch.optim.SGD, lr=0.05)
  
      all_expert_uids = [f"expert.{i}" for i in range(num_experts)]
      with background_server(
          expert_uids=all_expert_uids,
          device="cpu",
          optim_cls=SGD,
          hidden_dim=64,
          num_handlers=1,
      ) as server_peer_info:
          dht = DHT(start=True, initial_peers=server_peer_info.addrs)
  
          model = SwitchNetwork(dht, 64, 2, num_experts)
          opt = SGD(model.parameters(), lr=0.05)
  
          for step in range(max_steps):
              outputs, balancing_loss = model(X_train)
              loss = (
                  F.cross_entropy(outputs, y_train)
                  + 0.01 * balancing_loss
              )
              loss.backward()
              opt.step()
              opt.zero_grad()
  
              accuracy = (
                  (outputs.argmax(dim=1) == y_train)
                  .float()
                  .mean()
                  .item()
              )
              if accuracy >= threshold:
                  break
  
          assert (
              model.moe.grid_utilization.min().item()
              > (1 / num_experts) / 2
          )
          assert (
              accuracy >= threshold
          ), f"too small accuracy: {accuracy}"
  
file_path: hivemind/tests/test_training.py
imports: from functools import partial
  import pytest
  import torch
  import torch.nn
  import torch.nn.functional
  from sklearn.datasets import load_digits
  from hivemind import DHT
  from hivemind.moe.client import RemoteMixtureOfExperts, RemoteSwitchMixtureOfExperts
  from hivemind.moe.client.expert import create_remote_experts
  from hivemind.moe.expert_uid import ExpertInfo
  from hivemind.moe.server import background_server
label_desc: Use image node_id 3 for calling a global function with example usage: test_switch_training(max_steps, threshold, num_experts) without return types